(1) Technical Field
The present invention relates to the fields of cognitive psychology, biology, neural science, signal processing, neural networks, executive control, and computer science and, more specifically, to a system, method, and computer program product for dynamic task selection (executive control) suitable for mapping external inputs and internal goals toward actions that solve problems or elicit external rewards.
(2) Description of Related Art
Executive control is generally defined as the ability (of a person or an agent) to map external inputs and internal goals toward actions that solve problems or elicit rewards. That is, if a reward is gained by finding a goal object (such as an improvised explosive device (IED) or requested tool), then executive control must move a platform's sensors across its surroundings, potentially change those surroundings by locomotion, recognizing the goal object among potential distractions and clutter, and finally retrieve the object, all in a goal-direction fashion. Henceforth, the terms “executive control.” “dynamic task selection,” “decision making,” and “developing and refining reward-eliciting behaviors,” are generally used interchangeably.
Some examples of algorithms that use executive control include goal programming or adaptive search algorithms. Examples of applications for decision making vary from academic (e.g., Stroop Test, Wisconsin Card Sorting Task, or Towers of Hanoi) to applied (e.g. modeling of consumer decision making, medical diagnosis, command and control, modeling of human problem solving under stressful situations, and autonomous assembly robots).
In recent years, several bio-inspired methods for executive control have been reviewed, such as those discussed in “An Integrative Theory of Prefrontal Cortex Function”, Animal Review of Neuroscience, vol. 24, 2001, pp. 167-202 by E. K. Miller and J. D. Cohen. Recently there have also been several executive control architectures inspired by cognitive psychology (e.g. SOAR, ACT-R, etc.). In addition, it has been proven biologically that executive control is partly served by the Pre-Frontal Cortex (PFC). However, these previous bio-inspired methods for executive control typically only use simple inputs or limit themselves to a small subset of PFC functions. Another disadvantage of previous executive control architectures (inspired by cognitive psychology) is that they do not consider any detailed anatomical or physiological constraints.
Therefore, a system for executive control is needed that is inspired by both, cognitive psychology and biology, that uses and takes advantage of complex inputs, of a large subset of PFC functions, of detailed anatomical constrains, and of detailed physiological constraints, and which is time efficient and is versatile in its implementation and integration into larger scale systems and level applications.
The present invention provides a solution for “executive control” or “dynamic task selection” that uses components of previous models, particularly those of ARTSTORE, DIRECT, N-STREAMS, TELOS previously discussed in “Fast Learning VIEWNET Architectures for Recognizing 3-D Objects from Multiple 2-D Views”, Neural Networks, vol. 8, 1995, pp. 1053-1080 by G. Bradski, and S. Grossberg, discussed in “How Laminar Frontal Cortex and Basal Ganglia Circuits Interact to Control Planned and Reactive Saccades”, Neural Network, vol. 17, 2004, pp. 471-510 by in J. Brown, D. Bullock, and S. Grossberg, discussed in “A Self-Organizing Neural Model of Motor Equivalent Reaching and Tool Use by a Multijoint Arm”, Journal of Cognitive Neuroscience, vol. 5, 1993, pp. 408-435 by D. Bullock, and S. Grossberg, discussed in “Neural Dynamics of Attentionally-Modulated Pavlovian Conditioning: Conditioned Reinforcement, Inhibition and Opponent Processing”, Psychobiology, vol. 15, 1987, pp. 195-240 by S. Grossberg and N. A. Schmajuk, and discussed in “Neural Dynamics of Learning and Performance of Fixed Sequences: Latency Pattern Reorganizations and the N-STREAMS Model”, Boston University Technical Report CAS/CNS-02-005, Boston, Mass., United States, 2002 by B. Rhodes and D. Bullock.
However, the currently presented solution for executive control organizes those components in novel ways, while also addressing many more anatomical and physiological constraints than the previous bio-inspired or cognitive psychology-based executive control models. The method and system for executive control are based on a review of the structure and function of the mammalian prefrontal cortex (PFC). As a result, the executive control system and model provide a biologically plausible architecture that learns from and uses multimodal spatio-temporal working memories to develop and refine reward-eliciting behaviors. This model is defined by differential equations with timing constraints, and the implementation of the executive control model can be carried out on a serial digital computer (e.g. Von Neumann) or on a parallel analog computer (e.g. Analog Pulse Processing computer). Therefore, the model and consequently the system are time efficient and versatile in their implementation and integration into larger scale systems and level applications.
An additional advantage of the present method and system for “executive control” or “dynamic task selection” is that the model can be used with any algorithm requiring dynamic task selection or as part of a larger systems-level application. Therefore, the solution for executive control can also find applications in other executive control systems at large scales including multi-agent simulation.
Furthermore, since the present invention is as an all-software solution, the present solution for executive control can be easily and efficiently integrated into any system requiring decision making systems, such as systems for: modeling of consumer decision making; medical diagnosis: command and control; modeling of human problem solving, especially under stressful situations; and autonomous assembly robots.
For the foregoing reasons, there is a great need for a system for dynamic task selection (executive control) suitable for mapping external inputs and internal goals toward actions that solve problems or elicit external rewards, wherein the system is inspired by cognitive psychology and biology, which allows the system to use and take advantage of complex inputs, of a large subset of PFC functions, of detailed anatomical constrains, and of detailed physiological constraints, and allows the system to be time efficient and versatile in its implementation and integration into larger scale systems and level applications.